{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "every-detective",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Please install \"textblob\" and \"wordcloud\" from anaconda navigator\n",
    "\n",
    "#import some of packages/functions\n",
    "\n",
    "#Import plotting\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import math\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib.lines import Line2D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "3df3fa3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('C:/Users/owner/Documents/SPI/Duration/europe/2D density.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "502d59aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\owner\\AppData\\Local\\Temp\\ipykernel_6496\\3057124279.py:5: UserWarning: \n",
      "\n",
      "`shade_lowest` has been replaced by `thresh`; setting `thresh=0.05.\n",
      "This will become an error in seaborn v0.13.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(data=group_B_data, x='Duration', y='Intensity', cmap='Blues',shade=True, bw_adjust=0.9,  alpha=1,shade_lowest=False,   legend=True, )\n",
      "C:\\Users\\owner\\AppData\\Local\\Temp\\ipykernel_6496\\3057124279.py:5: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(data=group_B_data, x='Duration', y='Intensity', cmap='Blues',shade=True, bw_adjust=0.9,  alpha=1,shade_lowest=False,   legend=True, )\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "group_A_data = df[df['group'] == 'After 2010']\n",
    "group_B_data = df[df['group'] == 'Before 2010']\n",
    "\n",
    "# Create a KDE plot for group B first\n",
    "sns.kdeplot(data=group_B_data, x='Duration', y='Intensity', cmap='Blues',shade=True, bw_adjust=0.9,  alpha=1,shade_lowest=False,   legend=True, )\n",
    "\n",
    "# Create a KDE plot for group A on top of group B\n",
    "sns.scatterplot(data=group_A_data, x='Duration', y='Intensity', color=['red'], alpha=1,  legend=True,)\n",
    "\n",
    "# Add labels and a title\n",
    "plt.title('(b) Europe', loc='left', fontsize=22)\n",
    "plt.xlabel('', fontsize=20)\n",
    "plt.ylabel('', fontsize=20)\n",
    "\n",
    "# Create custom legend elements with colors\n",
    "#legend_elements = [\n",
    "   # Line2D([0], [2], color='blue', lw=8, label='Before 2010'),\n",
    "    #Line2D([0], [0], marker='o', color='w', label='After 2010',\n",
    "       #    markerfacecolor='red', markersize=10)]\n",
    "\n",
    "# Add the legend with custom legend elements\n",
    "#plt.legend(handles=legend_elements, title='', loc='upper right',fontsize= 18)\n",
    "\n",
    "# Save the plot with 300 DPI\n",
    "plt.savefig('europe.png', dpi=800)\n",
    "\n",
    "# Show the plot (optional)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f43cbc13",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\owner\\AppData\\Local\\Temp\\ipykernel_6496\\2380504518.py:1: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(data=df, x='Duration', y='Intensity', hue='group', cmap='Reds', shade=True, bw_adjust=0.8, z_order=1,dpi=300)\n",
      "C:\\Users\\owner\\new anaconda\\Lib\\site-packages\\seaborn\\distributions.py:1138: UserWarning: cmap parameter ignored when using hue mapping.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\Users\\owner\\new anaconda\\Lib\\site-packages\\seaborn\\distributions.py:1185: UserWarning: The following kwargs were not used by contour: 'z_order', 'dpi'\n",
      "  cset = contour_func(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(data=df, x='Duration', y='Intensity', hue='group', cmap='Reds', shade=True, bw_adjust=0.8, z_order=1,dpi=300)\n",
    "\n",
    "# Set the x-axis limits to start from 0\n",
    "#plt.xlim(0, df['Duration'].max())  # Assuming 'Duration' is non-negative\n",
    "# Add labels and a title\n",
    "plt.title('North America', loc='center', fontsize=22)\n",
    "plt.xlabel('Duration', fontsize=18)\n",
    "plt.ylabel('Intensity', fontsize=18)\n",
    "\n",
    "# Save the plot with 300 DPI\n",
    "plt.savefig('north america', dpi=300)\n",
    "\n",
    "# Show the plot (optional)\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
